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Defining and implementing a long-term vision and strategic roadmap for data engineering that aligns with broader business goals.
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Developing and enforcing data architecture strategies, standards, and best practices to maintain data integrity, availability, and security.
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Analyzing business needs and converting them into detailed ERD diagrams. You will also create, update, and manage ERDs using tools like Microsoft Visio, Lucidchart, or ER/Studio, ensuring they follow best practices in database design.
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Partnering with business stakeholders to understand their data needs and transform them into comprehensive data models and schemas.
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Designing and optimizing data storage solutions such as databases, data warehouses, and data lakes to meet the organization's evolving data requirements.
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Establishing and enforcing data governance policies to ensure consistent, high-quality data and compliance with relevant regulations.
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Leading the selection and evaluation of data management tools and technologies based on scalability, performance, and cost considerations.
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Managing the implementation and upkeep of data integration processes, including ETL (extract, transform, load) pipelines.
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Providing technical leadership and mentorship to junior data architects and engineers, fostering a culture of continuous learning and development within the team.
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Collaborating with IT teams to ensure data systems operate efficiently, addressing any performance issues or bottlenecks.
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Staying informed on industry trends and emerging technologies in data management, recommending innovative solutions to improve data architecture.
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Master's degree in Computer Science, Information Technology, Engineering, Mathematics, or Statistics.
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Strong expertise in data modelling and database design, with in-depth knowledge of relational, dimensional, and NoSQL databases.
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Proven experience with cloud platforms (AWS, Azure, GCP) and big data technologies (Databricks, Snowflake, Spark, PySpark, etc.).
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In-depth knowledge of data integration, including ETL processes, data pipelines, and synchronization techniques.
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Extensive understanding of data governance principles, data quality management, and compliance with data privacy regulations.
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Proficiency in programming languages such as Python, Java, or Scala.
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Familiarity with DevOps principles and CI/CD tools (e.g., GitHub, Jenkins).
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Strong analytical and problem-solving abilities, with the skill to translate complex business requirements into technical solutions.
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Excellent communication and interpersonal skills, capable of working effectively with both technical and non-technical teams.
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Demonstrated leadership experience, with a proven ability to mentor and guide teams.
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Experience with Azure Data Factory, Databricks, and Snowflake.
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Familiarity with the Apache Spark ecosystem and technologies like PySpark and Scala.
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Experience working in Agile/Scrum environments.
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Knowledge or experience in developing and deploying production-quality ML or AI models.
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Experience in the healthcare industry, with exposure to EDI, HIPAA, HL7, and FHIR integration standards.